Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs.

Chang, Connie Y; Huber, Florian A; Yeh, Kaitlyn J; Buckless, Colleen; Torriani, Martin

Chang, Connie Y; Huber, Florian A; Yeh, Kaitlyn J; Buckless, Colleen; Torriani, Martin.

Afiliación

Chang CY; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA. cychang@mgh.harvard.edu.
Huber FA; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA.
Yeh KJ; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
Buckless C; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA.
Torriani M; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA.

Skeletal Radiol ; 52(7): 1377-1384, 2023 Jul.

Article en En | MEDLINE | ID: mdl-36651936

Asunto(s)

Redes Neurales de la Computación; Tomografía Computarizada por Rayos X; Humanos; Tomografía Computarizada por Rayos X/métodos; Huesos; Pelvis

Palabras clave

Computed tomography; Deep learning; Osteolysis; Vertebra

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Redes Neurales de la Computación Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Skeletal Radiol Año: 2023 Tipo del documento: Article

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